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ConformRadar

by com.conformradar
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find_rules

Read-onlyIdempotent

Search the register of French rules that apply to websites (about 160 rules, each read in the official text).

robot_coverage tells whether ConformRadar checks the rule automatically ("auto"), partly ("partial") or not at all ("self":
a human must check it). Rule texts are in French; use get_rule for the full obligation, texts, penalties and authority.

Args:
    sector: "vente-en-ligne" (online shop selling to consumers), "immobilier" (estate agency) or "entreprise" (any business site).
    theme: one of tous, vente-en-ligne, b2b, immobilier, sante, alimentation, alcool-tabac-jeux, professions, finance, tourisme,
        formation-emploi, automobile, influence, elections.
    query: words to look for in the title, obligation and legal references (French, accents optional), e.g. "avis clients", "L. 221-5".
    limit: maximum number of rules returned (1 to 200).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryNo
themeNo
sectorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, it discloses things annotations cannot: the registry size (~160), the robot_coverage field semantics ('auto'/'partial'/'self' and what 'self' means operationally), and that rule texts are French. It does not describe result shape or pagination behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded and the Args block is dense with no filler; every parameter gloss carries information. Slightly long, but the length is justified by 0% schema coverage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description at least explains the robot_coverage field the agent will encounter, which is the most important interpretation aid. It omits what else comes back per rule (title, id, legal references) and how to page beyond limit, leaving a small gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden and does: it glosses each sector value, enumerates the valid theme values (which the schema does not constrain at all), explains query matching against title/obligation/legal references with examples, and giving the limit bound of 1-200 that the schema lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: searches a register of ~160 French rules applying to websites, with the scope size quantified. It also implicitly separates itself from get_rule by framing itself as the search step and naming get_rule as the detail step.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent: use get_rule when the full obligation, texts, penalties and authority are needed, implying find_rules is for discovery. No explicit when-not-to-use or prerequisite guidance, but the sibling hand-off is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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